CINCH - Forschungszentrum für Gesundheitsökonomik

Abstract: The incidence of adiposity in the early years of life has outgrown the prevalence rate in older children and adolescents globally; however, the relationships between unemployment and weight are predominantly studied in adults. This study examines the relationship between changing economic conditions during the Irish recession and child weight. Fixed effect logistic regression is used to examine the effects of parental unemployment on weight using the Growing up in Ireland infant cohort from 2008 to 2013. This study is the first to use longitudinal anthropometric measurements to estimate the impact of parental unemployment on children’s weight before, during and after a recession. Child growth charts are used to quantify children according to overweight for BMI, weight for age, and weight for height measures. For BMI, the probability of a child being overweight is 6 percentage points higher if either parent has experienced unemployment. For weight for age the probability is of similar magnitude across several alternative growth charts and definitions of adiposity. The analysis is repeated, cross-sectionally, for physical activity and diet to clarify mechanisms of effect. The probability of a child consuming healthy food and physical activity with an implied cost is lower if either parent becomes unemployed. A focus on excess adiposity in the early years is of crucial importance as if current trends are not addressed a generation of children may grow up with a higher level of chronic disease.

We examine variation in hospital quality across ownership, market concentration and membership of a hospital system. We use a measure of quality derived from the penalties imposed on hospitals under the flagship Hospital Readmission Reduction Program. We employ a novel estimation approach that extracts latent hospital quality from panel data on penalties and addresses the problem of non-penalized hospitals in short panels. Our quality measure correlates strongly across penalized conditions and with other non-incentivized quality metrics. We document a robust and sizable for-profit quality gap, which is largely crowded-out by competition between hospital chains, particularly for high-quality hospitals.

Who benefits from public health insurance in Indonesia? A machine learning approach to estimate treatment effect heterogeneity

Researchers evaluating the effects of health policies are often interested in identifying individuals who would benefit most from a particular programme. Such analyses could provide evidence on whether a programme worked for the intended recipients, and help design the eligibility criteria of future programmes. Traditional approaches such as subgroup analyses are constrained by only considering a few, pre-specified effect modifiers, and can also be prone to cherry-picking by ad-hoc selection of subgroups. Recently proposed causal inference approaches that incorporate machine learning (ML) have the potential to help explore treatment-effect heterogeneity in a flexible yet principled way. In this talk I illustrate such an approach, Causal Forests (Athey et al. 2019), in an evaluation of the effect of public health insurance on health care utilisation of Indonesian women. I highlight the opportunities presented by the approach to identify subgroups where the impacts of having health insurance differ, and to estimate so-called conditional average treatment effects at the level of the individual. I also discuss the challenges of using this approach alongside non-randomised study designs, typical when evaluating large scale health policies.